The present disclosure relates generally to electrogram detection and analysis, such as may be performed in cardiac diagnostic and therapeutic procedures. More specifically, the present disclosure relates to a system and method for detecting, analyzing, and mapping Local Abnormal Ventricular Activities (LAVA) from electrogram data.
In Ventricular Tachycardia (VT) formation, Local Abnormal Ventricular Activities (LAVAs) represent surviving potentials from slow conducting channels within a scar. These LAVAs are present due to trapping of the depolarization wave along a slow, highly fibrotic conducting channel present within a dense scar. These trapped LAVAs usually have multiple entry and exit points to trigger a macroscopic re-entry, thus causing VT.
Substrate-based approaches for detecting LAVAs target delayed signals relative to the detected QRS-complex within the signal, which are often defined as late-potentials. Such approaches may miss a significant proportion of LAVA, however, particularly in the septum and other early-to-activate regions. This can be seen in the exemplary electrogram signals depicted in FIGS. 1A-1E. FIGS. 1A-1C, for example, depict the presence of early LAVAs fused and buried within the EGM QRS portion of the signal whereas FIGS. 1D-1E depict late LAVAs that are more observable on the lateral or epicardial side of the heart. It is hypothesized that elimination of LAVAs recorded during sinus rhythm or ventricular pacing would be feasible as an end point for VT ablation, and that complete elimination of LAVAs would lead to an increase in arrhythmia-free survival. Currently during an EP study, these LAVAs are manually tagged by EP physicians based on certain bipolar electrogram (EGM) characteristics. This manual annotation process can be quite tedious and difficult for the physician to perform, particularly for high density electrograms.